Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks.
Ziqi Tang,Ximei Zhang,Guangqian Yang,Guanghua Zhang,Yubin Gong,Ke Zhao,Juan Xie,Junjun Hou,Jia Hou,Bin Sun,Wang Zhao +10 more
TL;DR: CNN methods to segment RNP in RVO in FA images can help improve clinical workflow, and can be useful for further investigating the association between RNP and retinal disease progression, as well as for evaluating the optimal treatments for the management of RVO.
Journal ArticleDOI
A brief review of machine learning methods for RNA methylation sites prediction.
TL;DR: In this article , the authors comprehensively explore machine learning based approaches for predicting 10 types of methylation of RNA, which include m6A, m5C, m7G, 5hmC and m1A,m5U, m6Am, and so on.
Book ChapterDOI
Ocean Ecosystems Plankton Classification
Alessandra Lumini,Loris Nanni +1 more
TL;DR: This chapter proposes an automated plankton recognition system, which is based on deep learning methods combined with so-called handcrafted features, and demonstrates high classification accuracy of the proposed approach when compared with other classifiers on the same datasets.
Journal ArticleDOI
Deep eigen-filters for face recognition: Feature representation via unsupervised multi-structure filter learning
TL;DR: This paper proposes a novel three-stage approach for filter learning alternatively which learns filters in multiple structures including standard filters, channel-wise filters and point-wise filter which are inspired from variations of CNNs’ convolution operations.
Journal ArticleDOI
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
TL;DR: Wang et al. as discussed by the authors proposed a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.